In today's world, machine learning is heavily applied for a large variety of knowledge fields. One focus area is disease prevention, medical diagnosis and treatment. Doctors may be supported by cognitive computing systems for faster and more reliable diagnoses. Machine learning algorithms, in particular database (DB) learning, show better accuracy than traditional methods of diagnosis. Data sets used in medical imaging are three-dimensional (3-D) data sets which consist of two-dimensional (2-D) films—also known as slices—such as computerized tomography scans. The data volume is typically relatively high due to the required high color—but also black and white—resolution.
These data sets may share many similarities with other 3-D data sets in terms of data representation. Deep learning algorithms are among the most proven algorithms, especially convolution neural networks (CNN). Such methods require patient data to be in a specified format; however, most of the patient's data is not compliant with that. The number of images for a patient is huge—in the range of about 200 slices—and the varying number of 3-D images per patient (training data) may cause deep learning algorithm adoption to be extremely hard and time-consuming.